RS
mehdi feyzolahpour
Abstract
Drought is the most expensive weather event in the world after hurricanes. Early detection of drought and prediction of its occurrence will reduce costs and save human lives. In this research, in order to evaluate the best index in estimating moisture stress and drought, 8 indices NDVI, NDWI, VCI, SR, ...
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Drought is the most expensive weather event in the world after hurricanes. Early detection of drought and prediction of its occurrence will reduce costs and save human lives. In this research, in order to evaluate the best index in estimating moisture stress and drought, 8 indices NDVI, NDWI, VCI, SR, MSI, SIWSI, NDII and NMI were used and Shadgan Wetland was investigated in the period of 2018 to 2023. LST index was also used to check temperature changes. In order to determine the most suitable index, the Pearson correlation coefficient was estimated between the indices and the effectiveness of each index was shown on the Chadwick scale. Based on this, NDWI, MSI, NMI and LST indices have the highest correlation and based on Chadwick's scale in 5 cases, NDWI and MSI indices have a strong and very strong correlation and there is a strong correlation between these two indices at around 0.99 - It has been established. The correlation between LST and NDWI indices was also negative and was estimated at -0.73. Due to the dominance of semi-arid conditions in the region, vegetation-based indices have a very weak capability in drought estimation, and the correlation between NDVI and NDWI was around 0.05. Therefore, based on this, it can be concluded that in Shadgan wetland, indicators based on humidity and temperature stress have better capabilities in drought estimation than vegetation indicators.
behrouz sobhani; Leyla Jafarzadehaliabad; Vahid Safarianzengir
Volume 6, Issue 21 , March 2020, , Pages 181-202
Abstract
1-Introduction Drought is one of the most important natural disasters affecting agriculture and water resources, and its abundance is extremely high in arid and semi-arid regions (Shamsenya et ...
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1-Introduction Drought is one of the most important natural disasters affecting agriculture and water resources, and its abundance is extremely high in arid and semi-arid regions (Shamsenya et al., 2008: 165). Drought is a natural phenomenon that has a complex process due to the interactions of various meteorological factors and occurs in all climatic conditions and in all regions of the planet (Samandianfard & Asadi, 2017). According to the domestic and foreign studies, many researchers have conducted research on drought monitoring and prediction, but the research that can show the drought phenomenon more accurately with the future vision is not takenhas not been conducted if both do not cover the issue adequately. According to the researchers, this study was conducted to model, monitor and predict drought with the new method in Iran in this study.2-MethodologyIn this study, drought modelling in Iran was carried out using climatic data of rainfall, temperature, sunshine, relative humidity and wind speed monthly (for 6 and 12 months scale) for the period of 29 years (1990-2018). At 30 stations using the new TIBI architecture model, a fuzzy set of four indicators (SET, SPI, SEB, and MCZI) valid in the World Meteorological Organization was used. For modelling the new TIBI index, the climatic data were first normalized, then four indices (SET, SPI, SEB, and MCZI) were calculated separately and the fuzzy modelling of the four indices was performed in the Matlab software and eventually to prioritize the drought-affected areas, the multivariate decision-making model, TOPSIS was used.3-ResultsIn order to investigate the effect of drought fluctuations in drought conditions of stations, it is possible to determine the changes in the indicators (SET, SPI, SEB, and MCZI) in the TIBI index analysis. Considering the large number of stations studied, For better understanding, only the drought series diagrams were presented at Bojnourd station on two 6 and 12 month scales (Figures 7 and 8),, (in the mentioned figures, the red arrow shows the drought margin at a 6-month scale with a value of 0.44 and greater, and a value of 0.76 and greater within the 12-month scale. The analysis of these forms shows that at the 6-year and 12-month scale at Bojnourd station, the amount of evapotranspiration was similar in drought conditions, which decreased from April 1994 to February 1999, and after this month an increase was observed if the impact of rainfall on a 6-month scale is weaker than the 12-month scale. It means that from May 1993 to November 1997, an increasing trend followed by the same pattern, and the indicators (SET, SPI, SEB, and MCZI) affect the TIBI index and show some trends, indicating that the new TIBI fuzzy index reflects the four indicators well. The T.I.B.I index at the 6-month scale shows a sharper shape than the scale 12.Prioritization of the stations involved in drought in Iran was analyzed using the TOPSIS model. The results of the TOPSIS model implementation using the degree of importance of the criteria derived from the entropy method indicate that, in terms of drought, more and fewer places are involved with drought by combining the two 6 and 12-month scale. According to the TOPSIS multivariate decision-making model, it was determined that the three stations most affected by drought based on the TOPSIS model were Bandar Abbas, Ahvaz and Bushehr, respectively, in the south and southwest regions of Iran with priority points of score (1, 0.78, and 0.62 respectively), and the three stations of Gorgan, Shahrekord and Orumieh in the northern and western parts of Iran with the scores of 0.026, 0.033 and 0.035 had lower priorities for drought response, respectively (Table 6) and (Figure 11).4-Discussion and conclusionDrought is a natural disaster that is gradually evolving under the influence of climatic anomalies over a long period of time. In recent years, various parts of the Middle East have faced drought, including those regions of Iran in Southwest Asia. In this study, drought phenomenon was assessed at 6 and 12 months using the new fuzzy index T.I.B.I. The results of the research showed that the total frequency of droughts in the 12-month scale was more than 6 months but the severity of a 6-month-old drought is more than 12 months old. On a 12-month scale, the number of drought repetitions is more than 6 months. Drought persistence was higher at 12-month scale, droughts were shorter at short-term and affected by temperature parameter. However, the intensity of drought over a long period of time had a slower response to rainfall changes. The highest percentage of drought incidence in scale of 6 months; Bandar Abbas, Bushehr, Ahvaz and Zahedan stations in the southern half of the study area respectively with the of drought (16.62, 11.24, 14.13 and 62.6 and the lowest in the 6-month scale were Urmia and Ardebil stations, with the percentages of 1.10 and 1.88, Ilam and Yasuj with the drought frequency of 1.61 and 2.01, Rasht and Gorgan, with a high percentage of drought frequency (1.26 and 0.87) in the northern and western part of Iran. The highest percentage of drought occurrence in scale 12; Bandar Abbas and Bushehr stations respectively with drought frequency of 24.30 and 14.83, Ahvaz with drought severity of 18.47, Kerman with 6.74 percent of drought frequencies in the south and southwest of Iran and the lowest in the 6-month scale; stations of Birjand (1.70), Bojnurd (66.6), Urmia (1.17), and Tabriz (66.2) in the northwest of Iran, Rasht (0.58), Sari (0.78) in the northern part of Iran.
hassan torabipodeh; Babak Shahinejad; Reza Dehghani
Volume 5, Issue 14 , June 2018, , Pages 179-197
Abstract
Background and Objective
Drought is one of the phenomena of climate that occurs in all climatic conditions and in all parts of the planet. Drought prediction has an important role in designing and managing natural resources, water resource systems, and determining the plant's water requirement. For ...
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Background and Objective
Drought is one of the phenomena of climate that occurs in all climatic conditions and in all parts of the planet. Drought prediction has an important role in designing and managing natural resources, water resource systems, and determining the plant's water requirement. For estimating drought, various approaches have been introduced in hydrology that artificial models are the most important ones. In this study for evaluating the accuracy of the models in estimating the 12-month standard rainfall index, monthly data from four weather stations in Boroujerd, Dorood, Selseleh and Dolphan in Lorestan province have been used. For modeling of drought in these stations utilized wavelet neural network and artificial neural network models and the results were compared to each other for the accuracy of the studied models. In a few studies, each of the models presented in the drought estimation has been studied. But the purpose of this research is simultaneous analysis of these models at four stations for estimating the standard rainfall index.
Methods
In this study, Boroujerd, Dorood, Selseleh and Dolphan that located in Lorestan province have been selected as the study area During the statistical period, the precipitation parameter was used at monthly time scale (1962-1372) for input and standard rainfall index as the output parameter of the models. For this purpose, at first 80% of the data (1372-1382) were selected for calibration of the models and 20% of the data (2012-2013) were used to validate the models. The wavelet neural network, which has a very good fit with the sinusoidal equations by separating the signal into high and low frequencies, can greatly increase the accuracy of the model and reduce noise. Artificial neural networks are inspired by the brain information processing system that ability to approximate patterns of a model has increased the scope of these networks. Correlation coefficient, root mean square error and mean absolute error value were used for evaluation and performance of the models.
Results
The results showed that both models have good performance in estimating the standard rainfall index in the four stations studied. Also, according to the evaluation criteria, the wavelet neural network model was found to have the highest accuracy and low error rate compared to the artificial neural network model.
Conclusions
In total, the results showed that the use of wavelet neural network model can be effective in estimating the standard rainfall index. also It can be useful in facilitating the development and implementation of management strategies to prevent drought and is a step in making managerial decisions to improve water resources.
Reza Ghazavi; Majid Ramezani
Volume 4, Issue 12 , December 2017, , Pages 111-129
Abstract
Extend Abstract Introduction Groundwater is one of the most important resources of fresh water in the world, especially in arid and semi-arid areas. In these areas, the demand for groundwater has increased due to the decline of rainfall, population growth, and industrialization, while its quality has ...
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Extend Abstract Introduction Groundwater is one of the most important resources of fresh water in the world, especially in arid and semi-arid areas. In these areas, the demand for groundwater has increased due to the decline of rainfall, population growth, and industrialization, while its quality has declined via industrial and urban contamination. The removal of the groundwater pollution is very costly and time-consuming. Consequently, the prevention of the groundwater contamination is the best way for groundwater protection. The main aim of this study was to investigate the trend of groundwater quality and quantity changes in the Rafsanjan plain in relation to the groundwater discharge and rainfall change. Methodology The study area is the Rafsanjan plain with an area of 5459.36 km2 (with an altitude of 45°, 30¢ to 56°, 30¢ and latitude of 29°, 59¢ to 29°, 15¢). In this study, the essential maps including topography, drainage, piezometric wells location, and groundwater quality and quantity maps were created using GIS10.1. The groundwater level in 80 pizometric wells and the groundwater quality in 50 wells were investigated and analyzed for a period of 10 years (2002-2012). The groundwater unit hydrograph and rainfall pattern were compared to indicate the impacts of rainfall variability and the groundwater over-extraction on the groundwater level variation. Water quality maps were created using Vilcox method. Based on kriging interpolation method, the quantitative and qualitative maps of the study area were prepared using geographic information system (GIS). Results The groundwater hydrograph of the study plain indicated that the groundwater level declined continuously. As during the past 10 years, the groundwater decline was 8 m, so the annual groundwater decline in the study plain was 0.8 m. comparing the groundwater level of 2002 and 2012 via piezometric wells indicated a significant decline of the groundwater level. In 2002, for 81% of the study plain, the groundwater level was between 30-90 m, while it declined to 68% in 2012. The maximum groundwater decline was related to the area where groundwater level in 2002 was between 30 and 60 m. The area where the groundwater level was between 90 to 120 m, it increased from 683.8 km2 in 2002 to 999.7 km2 in 2012. Also the area where groundwater level was more than 120 m, it increased by 5.3%. A significant relationship was observed between the groundwater level and the volume of the groundwater extraction in 10 years of the study (R2 = 0.6). However, no significant relationship was observed between the groundwater level and the average rainfall between 2002 and 2012 (R2 = 0.04). These results indicated that the impact of the groundwater extraction on the groundwater level decline was more important than the rainfall change. In this study, Wilcox method was used for the investigation of the variability of the groundwater quality. Based on Electric conductivity (EC) and Sodium absorption rate (SAR) in Wilcox method, 16 classes of groundwater quality should be investigated. According to these results, in 10 years of the study period, the number of wells located in C3S2 and C4S2 classes of groundwater quality declined by 2 and 4% respectively. The number of wells located in C4S4 increased from 33% in 2002 to 38% in 2012. Cumulative discharge of all study wells decreased from 610 liter per second to 469 liter per second. The maximum decline was related to C4S3 and C4S2 groups. Discussion The results of this study indicated that the groundwater quality and level declined in the study area. According to the results of the water quality maps, the area of the aquifer with groundwater quality located in C3S2 and C4S2 respectively decreased by 6 and 1.4 %, while the area of the aquifer with groundwater quality located in C4S4 increased by 4.5 percent. The study of the piezometric wells with a depth of 30 m and less indicated that 15% of these wells dried between 2002 and 2012 due to groundwater level declination. The water quality of the profound wells (with a depth of 31 to 200m) decreased by 8.5%. These results indicate that the groundwater quality decreases with increasing of the groundwater level. Conclusion According to these results, the groundwater decline due to the rainfall decline, and the role of the groundwater abstraction in the agricultural area were more important than the rainfall deficits. The qualitative and quantitative maps of groundwater were also prepared via kriging interpolation method and GIS. Based on these results, it can be suggested that rainfall decline leads to the decline of groundwater, but excessive removal of groundwater resources in agricultural lands is a major factor that should reduce the quality of the groundwater in the study area.
Mohammadtaghi Sattari; Rasoul Mirabbai Najafabadi; Masood Alimohammadi
Volume 3, Issue 8 , December 2016, , Pages 73-92
Abstract
Received: 2015.08.16 Accepted: 2016.11.18 Mohammadtaghi Sattari[1]* Rasoul Mirabbasi Najafabadi[2] Masood Alimohammadi[3] Abstract Accurate prediction of droughts in arid and semi-arid countries, like Iran, have important role in water resources management and designing appropriate plans for coping with ...
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Received: 2015.08.16 Accepted: 2016.11.18 Mohammadtaghi Sattari[1]* Rasoul Mirabbasi Najafabadi[2] Masood Alimohammadi[3] Abstract Accurate prediction of droughts in arid and semi-arid countries, like Iran, have important role in water resources management and designing appropriate plans for coping with drought consequences. Since the standardized precipitation index (SPI) is known as a suitable index for drought analysis, in this study, we used the M5 rule tree model for forecasting SPI values. For this purpose, the monthly precipitation data of Maragheh synoptic station were used during a 25-year period for calculating SPI values at 6-month time scale (SPI-6). The results indicated that the Maragheh region was faced with successive and severe droughts in recent two decays. In the next step, the SPI-6 values were forecasted for next 1 to 12 months using M5 rule tree model. The results showed that the SPI-6 values in previous time steps had the most effect on forecasting the next SPI-6 values, and the forecasting accuracy decreases with increasing prediction length. So the correlation coefficient of forecasting SPI-6 for next month was obtained 0.94 which this value was decreased to about 0.40 for forecasting SPI-6 for next 12 months. However, the M5 rule tree model provides more understandable, applicable and simple linear relation in forecasting droughts and shows relatively good performance and accuracy. [1]- Assistant Professor, Department of Water Engineering, University of Tabriz (Corresponding Autor), Email:mail:mtsattar@gmail.com. [2]- Assistant Professor, Department of Water Engineering, Shahrekord University. [3]- MSc of Civil Engineering.
